Image dataset for classification of diseases in guava fruits and leavesMendeley Data
Guava (Psidium guajava) this is a tropical fruit and one of the common tropical fruits in Bangladesh. The economic and health value of this important crop is unmeasurable, but it quickly becomes infected with many diseases that can greatly reduce its yield and quality. Thus, the use of technology fo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925001106 |
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| Summary: | Guava (Psidium guajava) this is a tropical fruit and one of the common tropical fruits in Bangladesh. The economic and health value of this important crop is unmeasurable, but it quickly becomes infected with many diseases that can greatly reduce its yield and quality. Thus, the use of technology for automatic fruit and leaf disease detection is necessary in agriculture. The dataset provides us overall guava fruits & leaves image samples for detection of diseases in guava fruits and leaves. This dataset consists of images of healthy and diseased samples infected by fruit disease such as anthracnose, scab, styler end root and leaf disease such as canker, rust, anthracnose and dot. It consists of 3,432 real images obtained from different places in Bangladesh. It is also extended 20,344 augmented images ready to be used for machine learning purposes. This dataset serves as a fundamental building block for utilizing machine learning and computer vision techniques to develop automated detection systems of various diseases. It assists in the early detection of diseases affecting guava, and provides them with solutions to intervene there itself saving agricultural yield and nutritional losses while also promoting sustainable farming practices. This dataset will assist researchers for progressing guava detecting disease through the execution of computational models and application of better machine learning techniques. |
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| ISSN: | 2352-3409 |